Why MySQL beats MongoDB
|
|
- Evangeline Waters
- 8 years ago
- Views:
Transcription
1 SEARCH MARKETING EVOLVED Why MySQL beats MongoDB Searchmetrics in one big data scenario Suite Stephan Sommer-Schulz (Director Research) Searchmetrics Inc. 2011
2 The Challenge Backlink Database: Build a graph of most of the backlinks in the internet. Store the data in a database Give the user the possibilities to Perform queries with multiple where conditions Aggregate fields where needed (grouping, counting) Order results over multiple fields (asc, desc) Answer requests within seconds Keep costs and administration as low as possible Definition: A backlink is an internet link which points from one webpage to another, which is not in the same subdomain (means: no internal links). 2
3 The Data Graph components: The graph consists of nodes (webpages) and edges (links). Each node and each link can have a number of attributes. No. of pages: No. of links: No. of daily new links & updates: Because the links are the most interesting thing in our scenario, we will have at least one record per link in our database (> 112 billion). 3
4 MongoDB Approach (v2.0.0) Good: Buildin sharding! Replication (not needed here) Rest and json (or bson) Schemaless / document oriented (we will see) Scalable (we will see) Not so good: Poor datatypes (no tinyint, no smallint,...) Large datasize, because of document-orientation, field names,... Performance issues on aggregates (e.g. count distinct) Indexes must fit completely into memory Funny problems with sharding keys if a shard gets to big, but cannot be divided anymore. 4
5 MongoDB Tests 1 Test Sharding / Datainput / Index 3 Sharding-Servers (AMD Hexacore, 8 GByte, SSD) Start with one index Input 1 billion records (~ million / shard) Create a second index... Problem 1: On one shard the second index didn t fit into memory (the eta was several days!) Do a db.killop(...) for the index creation process process is marked as killed, but no other reaction after 6 hours, system is blocked L Kill the mongod process, install more Memory (16 GByte) create index in 3 hours. Problem 2: Sharding-Key has to be changed (no possibility of dividing any more in a segment) Export data reimport data waisting lots of time and resources Clue: Find a key that fits your needs over years, even if the requierments change sometimes (and they will) -> Impossible Mission in very large systems Problem 3: The size of data and index (disk and memory) is much bigger than expected!? 5
6 MongoDB Tests 2 Test Advanced Queries 3 Sharding-Servers (AMD Hexacore, 8 GByte, SSD) Input 1 billion records (~ million / shard) Problem 1: Counting rows with distinct is damn slow Tried to calculate the counters over night doesn t fit all needs, because users can influence the queries. Map/Reduce is not an option for just in-time results Problem 2: Using query operators on none index fields is very slow, need to keep data in memory as well as the indexes (more memory!) It is not possible to catch every query-situation with an index, if we don t want to spend lots of money for Schemaless is fine, but the need of indexes in memory looks like a blocker, MySQL has not. 6
7 MySQL Approach (v5.5) Good: Well known and powerfull SQL Replication (not needed here) Good datatypes Small data on disk because of relations Smaller indexes because of better datatypes Not so good: No sharding! No rest, no json More development on application-site needed (watch for costs) 7
8 Datasize Comparision (not relational) Fields Type (MySQL) Avg. Bytes (MySQL) Type (MongoDB) Avg. Bytes (MongoDB) 6x Textfields Varchar(3-1000) 346 Varchar (3-1000) 346 6x Tiny Attributes Tinyint (each 1Byte) 6 64-Bit Integer 48 6x Small Attributes Smallint (each 2 Bytes) Bit Integer 48 10x Numbers Integer (each 4 Bytes) Bit Integer 80 3x Large Numbers Bigints (each 8 Byte) Bit Integer 24 2x Doubles Double (each 8 Byte) 16 Float (each 8 Bytes) 16 6x Relational Ids 6 Ints, 2 Bigints Sharding-Key x 64-Bit Integer 32 Fieldnames x 2 Bytes 66 Sum per record: 482 Bytes 660 Bytes Relational: MySQL relational datasize (4 tables): 148 Bytes per record! 8
9 Datasize Comparision (real world) MySQL: One Record: 148 Byte (relational) No. of records: 112 Billion Datasize: 15.1 Terabyte Indexsize: 5.5 Terabyte MongoDB: One Record: 660 Byte (document) No. of records: 112 Billion Datasize: 67.2 Terabyte Indexsize: 11.4 Terabyte Looks like we will run into a little cost-problem with MongoDB... 9
10 MongoDB Datasize Fieldname Problem: Fieldnames are stored in MongoDB with every single datarecord that s necessary, because it is schemaless! Fieldnames are stored in every row uncompressed If 1 datarecord consists of 33 fields in our scenario and the fieldnames are only 2 Bytes long ( aa, ab, ac,...) we will need: One Datarecord Fieldname-Size: No. Fields: 33 Fieldbytes/Record: 66 (with only 2 Bytes / Field) 112 Billion Datarecords Fieldname-Size: 66 Byte * 112 Billion = 6.72 Terabyte! (only for the names) This is where part of your diskspace goes in MongoDB. Maybe schemaless is not that big advantage... 10
11 Price on Amazon Cloud (EC2) in Ireland MySQL: Datasize: Indexsize: 15.1 Terabyte 5.5 Terabyte Server: 107 (with 75% mem for index) $/Month: $ ,- (admin & traffic costs come on top) MongoDB: Datasize: 68.5 Terabyte Indexsize: 11.4 Terabyte Server: 221 (with 75% mem for index) $/Month: $ ,- (admin & traffic costs come on top) Amazon EC2 High-Memory Quadrupel Extra Large Instance Memory: 68,4 Gbyte Disk: 1.6 Terabyte $/Hour: $ 2,024 $/Month: $ 1.480,- 11
12 MySQL Challenge Assumption: Sharding in MySQL is not so difficult if we can avoid some traps: ToDo: No cross-shard joining No cross-shard distinct counting Generate unique keys in application Find a schema, which fits the above criteria (we luckily found one, after two weeks in the thinktank, but there will be usecases which never will work in such a setup). Setup shards as standalone MySQL servers, with no dependencies Build a data-deployment mechanism for the shards (Remind: unique ids) Build an application which is able to request data parallel from the shards and than make aggregations, sorts,... Problems: Network latency Caching in application level is finegrained, but can be tricky. 12
13 We are hiring Mad Scientist* (m/w): You ve trained a Support Vector Machine which selects your morning flakes by weather, milk temperature, blood pressure and thirty other features. TF/IDF and BM25 are so 90s. When you are looking for your doorkey, your searchengine ranks listwise. You didn t join the Yahoo Learning to Rank challenge because you wouldn t want to be a party pooper With the approach of map/reduce you gave a lol about the new funny name of the good old scatter/gather Your brain is parted in stack and heap, programming a doom-clone in whitespace was fun for an afternoon. The Mad Scientist is just another step on your way to Evil Genius. Hundrets of servers, billions of datasets, sharding, replication, coffee, pizza, coke, more coffee and colleagues dressed in black. Welcome to the real world, Neo. *jobtitle may differ on business card 13
14 Thanks a lot! Searchmetrics Inc. 2011
In Memory Accelerator for MongoDB
In Memory Accelerator for MongoDB Yakov Zhdanov, Director R&D GridGain Systems GridGain: In Memory Computing Leader 5 years in production 100s of customers & users Starts every 10 secs worldwide Over 15,000,000
More informationThe MongoDB Tutorial Introduction for MySQL Users. Stephane Combaudon April 1st, 2014
The MongoDB Tutorial Introduction for MySQL Users Stephane Combaudon April 1st, 2014 Agenda 2 Introduction Install & First Steps CRUD Aggregation Framework Performance Tuning Replication and High Availability
More informationIntegrating Big Data into the Computing Curricula
Integrating Big Data into the Computing Curricula Yasin Silva, Suzanne Dietrich, Jason Reed, Lisa Tsosie Arizona State University http://www.public.asu.edu/~ynsilva/ibigdata/ 1 Overview Motivation Big
More informationCan the Elephants Handle the NoSQL Onslaught?
Can the Elephants Handle the NoSQL Onslaught? Avrilia Floratou, Nikhil Teletia David J. DeWitt, Jignesh M. Patel, Donghui Zhang University of Wisconsin-Madison Microsoft Jim Gray Systems Lab Presented
More informationWhy NoSQL? Your database options in the new non- relational world. 2015 IBM Cloudant 1
Why NoSQL? Your database options in the new non- relational world 2015 IBM Cloudant 1 Table of Contents New types of apps are generating new types of data... 3 A brief history on NoSQL... 3 NoSQL s roots
More informationFour Orders of Magnitude: Running Large Scale Accumulo Clusters. Aaron Cordova Accumulo Summit, June 2014
Four Orders of Magnitude: Running Large Scale Accumulo Clusters Aaron Cordova Accumulo Summit, June 2014 Scale, Security, Schema Scale to scale 1 - (vt) to change the size of something let s scale the
More informationIntroduction to SQL for Data Scientists
Introduction to SQL for Data Scientists Ben O. Smith College of Business Administration University of Nebraska at Omaha Learning Objectives By the end of this document you will learn: 1. How to perform
More informationMongoDB and Couchbase
Benchmarking MongoDB and Couchbase No-SQL Databases Alex Voss Chris Choi University of St Andrews TOP 2 Questions Should a social scientist buy MORE or UPGRADE computers? Which DATABASE(s)? Document Oriented
More informationEvolution of Web Application Architecture International PHP Conference. Kore Nordmann / @koredn / <kore@qafoo.com> June 9th, 2015
Evolution of Web Application Architecture International PHP Conference Kore Nordmann / @koredn / June 9th, 2015 Evolution Problem Too many visitors Evolution Evolution Lessons Learned:
More informationCloud Scale Distributed Data Storage. Jürmo Mehine
Cloud Scale Distributed Data Storage Jürmo Mehine 2014 Outline Background Relational model Database scaling Keys, values and aggregates The NoSQL landscape Non-relational data models Key-value Document-oriented
More informationNoSQL in der Cloud Why? Andreas Hartmann
NoSQL in der Cloud Why? Andreas Hartmann 17.04.2013 17.04.2013 2 NoSQL in der Cloud Why? Quelle: http://res.sys-con.com/story/mar12/2188748/cloudbigdata_0_0.jpg Why Cloud??? 17.04.2013 3 NoSQL in der Cloud
More informationOverview of Databases On MacOS. Karl Kuehn Automation Engineer RethinkDB
Overview of Databases On MacOS Karl Kuehn Automation Engineer RethinkDB Session Goals Introduce Database concepts Show example players Not Goals: Cover non-macos systems (Oracle) Teach you SQL Answer what
More informationMoving From Hadoop to Spark
+ Moving From Hadoop to Spark Sujee Maniyam Founder / Principal @ www.elephantscale.com sujee@elephantscale.com Bay Area ACM meetup (2015-02-23) + HI, Featured in Hadoop Weekly #109 + About Me : Sujee
More informationBenchmarking Couchbase Server for Interactive Applications. By Alexey Diomin and Kirill Grigorchuk
Benchmarking Couchbase Server for Interactive Applications By Alexey Diomin and Kirill Grigorchuk Contents 1. Introduction... 3 2. A brief overview of Cassandra, MongoDB, and Couchbase... 3 3. Key criteria
More informationHow To Scale Out Of A Nosql Database
Firebird meets NoSQL (Apache HBase) Case Study Firebird Conference 2011 Luxembourg 25.11.2011 26.11.2011 Thomas Steinmaurer DI +43 7236 3343 896 thomas.steinmaurer@scch.at www.scch.at Michael Zwick DI
More informationAnalytics March 2015 White paper. Why NoSQL? Your database options in the new non-relational world
Analytics March 2015 White paper Why NoSQL? Your database options in the new non-relational world 2 Why NoSQL? Contents 2 New types of apps are generating new types of data 2 A brief history of NoSQL 3
More informationTurn Big Data to Small Data
Turn Big Data to Small Data Use Qlik to Utilize Distributed Systems and Document Databases October, 2014 Stig Magne Henriksen Image: kdnuggets.com From Big Data to Small Data Agenda When do we have a Big
More informationHow To Handle Big Data With A Data Scientist
III Big Data Technologies Today, new technologies make it possible to realize value from Big Data. Big data technologies can replace highly customized, expensive legacy systems with a standard solution
More informationBig Systems, Big Data
Big Systems, Big Data When considering Big Distributed Systems, it can be noted that a major concern is dealing with data, and in particular, Big Data Have general data issues (such as latency, availability,
More informationBig Data & Data Science Course Example using MapReduce. Presented by Juan C. Vega
Big Data & Data Science Course Example using MapReduce Presented by What is Mongo? Why Mongo? Mongo Model Mongo Deployment Mongo Query Language Built-In MapReduce Demo Q & A Agenda Founders Max Schireson
More informationGetting Started with MongoDB
Getting Started with MongoDB TCF IT Professional Conference March 14, 2014 Michael P. Redlich @mpredli about.me/mpredli/ 1 1 Who s Mike? BS in CS from Petrochemical Research Organization Ai-Logix, Inc.
More informationHadoop and MySQL for Big Data
Hadoop and MySQL for Big Data Alexander Rubin October 9, 2013 About Me Alexander Rubin, Principal Consultant, Percona Working with MySQL for over 10 years Started at MySQL AB, Sun Microsystems, Oracle
More informationAlexander Rubin Principle Architect, Percona April 18, 2015. Using Hadoop Together with MySQL for Data Analysis
Alexander Rubin Principle Architect, Percona April 18, 2015 Using Hadoop Together with MySQL for Data Analysis About Me Alexander Rubin, Principal Consultant, Percona Working with MySQL for over 10 years
More informationCOSC 6397 Big Data Analytics. 2 nd homework assignment Pig and Hive. Edgar Gabriel Spring 2015
COSC 6397 Big Data Analytics 2 nd homework assignment Pig and Hive Edgar Gabriel Spring 2015 2 nd Homework Rules Each student should deliver Source code (.java files) Documentation (.pdf,.doc,.tex or.txt
More informationHow Lucene Powers LinkedIn Segmentation & Targeting Platform
How Lucene Powers LinkedIn Segmentation & Targeting Platform Lucene/SOLR Revolution EU, November 2013 Hien Luu, Raj Rangaswamy About Us * Hien Luu Rajasekaran Rangaswamy Agenda Little bit about LinkedIn
More informationHadoop: Embracing future hardware
Hadoop: Embracing future hardware Suresh Srinivas @suresh_m_s Page 1 About Me Architect & Founder at Hortonworks Long time Apache Hadoop committer and PMC member Designed and developed many key Hadoop
More informationReal-time reporting at 10,000 inserts per second. Wesley Biggs CTO 25 October 2011 Percona Live
Real-time reporting at 10,000 inserts per second Wesley Biggs CTO 25 October 2011 Percona Live Agenda 1. Who we are, what we do, and (maybe) why we do it 2. Solution architecture and evolution 3. Top 5
More informationSummary of Alma-OSF s Evaluation of MongoDB for Monitoring Data Heiko Sommer June 13, 2013
Summary of Alma-OSF s Evaluation of MongoDB for Monitoring Data Heiko Sommer June 13, 2013 Heavily based on the presentation by Tzu-Chiang Shen, Leonel Peña ALMA Integrated Computing Team Coordination
More information5 Signs You Might Be Outgrowing Your MySQL Data Warehouse*
Whitepaper 5 Signs You Might Be Outgrowing Your MySQL Data Warehouse* *And Why Vertica May Be the Right Fit Like Outgrowing Old Clothes... Most of us remember a favorite pair of pants or shirt we had as
More informationBig Data. Facebook Wall Data using Graph API. Presented by: Prashant Patel-2556219 Jaykrushna Patel-2619715
Big Data Facebook Wall Data using Graph API Presented by: Prashant Patel-2556219 Jaykrushna Patel-2619715 Outline Data Source Processing tools for processing our data Big Data Processing System: Mongodb
More informationSQL: joins. Practices. Recap: the SQL Select Command. Recap: Tables for Plug-in Cars
Recap: the SQL Select Command SQL: joins SELECT [DISTINCT] sel_expression [, sel_expression ] FROM table_references [WHERE condition] [GROUPBY column [,column ] [[HAVING condition]] [ORDER BY columns [ASC
More informationNot Relational Models For The Management of Large Amount of Astronomical Data. Bruno Martino (IASI/CNR), Memmo Federici (IAPS/INAF)
Not Relational Models For The Management of Large Amount of Astronomical Data Bruno Martino (IASI/CNR), Memmo Federici (IAPS/INAF) What is a DBMS A Data Base Management System is a software infrastructure
More informationThe Hadoop Framework
The Hadoop Framework Nils Braden University of Applied Sciences Gießen-Friedberg Wiesenstraße 14 35390 Gießen nils.braden@mni.fh-giessen.de Abstract. The Hadoop Framework offers an approach to large-scale
More informationMark Bennett. Search and the Virtual Machine
Mark Bennett Search and the Virtual Machine Agenda Intro / Business Drivers What to do with Search + Virtual What Makes Search Fast (or Slow!) Virtual Platforms Test Results Trends / Wrap Up / Q & A Business
More informationScalable Architecture on Amazon AWS Cloud
Scalable Architecture on Amazon AWS Cloud Kalpak Shah Founder & CEO, Clogeny Technologies kalpak@clogeny.com 1 * http://www.rightscale.com/products/cloud-computing-uses/scalable-website.php 2 Architect
More informationBig Data on AWS. Services Overview. Bernie Nallamotu Principle Solutions Architect
on AWS Services Overview Bernie Nallamotu Principle Solutions Architect \ So what is it? When your data sets become so large that you have to start innovating around how to collect, store, organize, analyze
More informationOn- Prem MongoDB- as- a- Service Powered by the CumuLogic DBaaS Platform
On- Prem MongoDB- as- a- Service Powered by the CumuLogic DBaaS Platform Page 1 of 16 Table of Contents Table of Contents... 2 Introduction... 3 NoSQL Databases... 3 CumuLogic NoSQL Database Service...
More informationPetabyte Scale Data at Facebook. Dhruba Borthakur, Engineer at Facebook, SIGMOD, New York, June 2013
Petabyte Scale Data at Facebook Dhruba Borthakur, Engineer at Facebook, SIGMOD, New York, June 2013 Agenda 1 Types of Data 2 Data Model and API for Facebook Graph Data 3 SLTP (Semi-OLTP) and Analytics
More informationMonetizing Millions of Mobile Users with Cloud Business Analytics
Monetizing Millions of Mobile Users with Cloud Business Analytics MicroStrategy World 2013 David Abercrombie Data Analytics Engineer Agenda Tapjoy Big Data Architecture MicroStrategy Cloud Implementation
More informationFarms, Fabrics and Clouds
Steve Loughran Julio Guijarro HP Laboratories, Bristol, UK December 2007 steve.loughran@hpl.hp.com julio.guijarro@hpl.hp.com Julio Guijarro Researcher at HP Laboratories Area of interest: Deployment In
More informationbigdata Managing Scale in Ontological Systems
Managing Scale in Ontological Systems 1 This presentation offers a brief look scale in ontological (semantic) systems, tradeoffs in expressivity and data scale, and both information and systems architectural
More informationReference Architecture, Requirements, Gaps, Roles
Reference Architecture, Requirements, Gaps, Roles The contents of this document are an excerpt from the brainstorming document M0014. The purpose is to show how a detailed Big Data Reference Architecture
More informationUnderstanding NoSQL Technologies on Windows Azure
David Chappell Understanding NoSQL Technologies on Windows Azure Sponsored by Microsoft Corporation Copyright 2013 Chappell & Associates Contents Data on Windows Azure: The Big Picture... 3 Windows Azure
More informationScaling Database Performance in Azure
Scaling Database Performance in Azure Results of Microsoft-funded Testing Q1 2015 2015 2014 ScaleArc. All Rights Reserved. 1 Test Goals and Background Info Test Goals and Setup Test goals Microsoft commissioned
More informationGraph Database Proof of Concept Report
Objectivity, Inc. Graph Database Proof of Concept Report Managing The Internet of Things Table of Contents Executive Summary 3 Background 3 Proof of Concept 4 Dataset 4 Process 4 Query Catalog 4 Environment
More informationRackspace Cloud Databases and Container-based Virtualization
Rackspace Cloud Databases and Container-based Virtualization August 2012 J.R. Arredondo @jrarredondo Page 1 of 6 INTRODUCTION When Rackspace set out to build the Cloud Databases product, we asked many
More informationHow To Create A Table In Sql 2.5.2.2 (Ahem)
Database Systems Unit 5 Database Implementation: SQL Data Definition Language Learning Goals In this unit you will learn how to transfer a logical data model into a physical database, how to extend or
More informationScaling with MongoDB. by Michael Schurter 2011 @schmichael. Scaling with MongoDB by Michael Schurter - OS Bridge, 2011.06.22
Scaling with MongoDB by Michael Schurter 2011 @schmichael What is MongoDB? Community Developer by 10gen AGPL Database Apache drivers JIRA issue tracking On GitHub What is MongoDB? Architecture Server Database
More informationSQL Server Table Design - Best Practices
CwJ Consulting Ltd SQL Server Table Design - Best Practices Author: Andy Hogg Date: 20 th February 2015 Version: 1.11 SQL Server Table Design Best Practices 1 Contents 1. Introduction... 3 What is a table?...
More informationSpark: Cluster Computing with Working Sets
Spark: Cluster Computing with Working Sets Outline Why? Mesos Resilient Distributed Dataset Spark & Scala Examples Uses Why? MapReduce deficiencies: Standard Dataflows are Acyclic Prevents Iterative Jobs
More informationComparing Scalable NOSQL Databases
Comparing Scalable NOSQL Databases Functionalities and Measurements Dory Thibault UCL Contact : thibault.dory@student.uclouvain.be Sponsor : Euranova Website : nosqlbenchmarking.com February 15, 2011 Clarications
More informationHow graph databases started the multi-model revolution
How graph databases started the multi-model revolution Luca Garulli Author and CEO @OrientDB QCon Sao Paulo - March 26, 2015 Welcome to Big Data 90% of the data in the world today has been created in the
More informationBig Data Management. Big Data Management. (BDM) Autumn 2013. Povl Koch September 2, 2013 01-09-2013 1
Big Data Management Big Data Management (BDM) Autumn 2013 Povl Koch September 2, 2013 01-09-2013 1 Overview Today s program 1. Little more practical details about this course 2. Chapter 2 & 3 in NoSQL
More informationNoSQL - What we ve learned with mongodb. Paul Pedersen, Deputy CTO paul@10gen.com DAMA SF December 15, 2011
NoSQL - What we ve learned with mongodb Paul Pedersen, Deputy CTO paul@10gen.com DAMA SF December 15, 2011 DW2.0 and NoSQL management decision support intgrated access - local v. global - structured v.
More informationsqlite driver manual
sqlite driver manual A libdbi driver using the SQLite embedded database engine Markus Hoenicka mhoenicka@users.sourceforge.net sqlite driver manual: A libdbi driver using the SQLite embedded database engine
More informationUnified Big Data Processing with Apache Spark. Matei Zaharia @matei_zaharia
Unified Big Data Processing with Apache Spark Matei Zaharia @matei_zaharia What is Apache Spark? Fast & general engine for big data processing Generalizes MapReduce model to support more types of processing
More informationDatabase Administration with MySQL
Database Administration with MySQL Suitable For: Database administrators and system administrators who need to manage MySQL based services. Prerequisites: Practical knowledge of SQL Some knowledge of relational
More informationIntro to Databases. ACM Webmonkeys 2011
Intro to Databases ACM Webmonkeys 2011 Motivation Computer programs that deal with the real world often need to store a large amount of data. E.g.: Weather in US cities by month for the past 10 years List
More informationDatabase Scalability with Parallel Databases
Database Scalability with Parallel Databases Boston MySQL Meetup Monday, December 10, 2012 What should I do while you yabber along for 1 hour? 1. Eat Pizza 2. Ask questions, and keep me honest 3. Tweet
More informationNear Real Time Indexing Kafka Message to Apache Blur using Spark Streaming. by Dibyendu Bhattacharya
Near Real Time Indexing Kafka Message to Apache Blur using Spark Streaming by Dibyendu Bhattacharya Pearson : What We Do? We are building a scalable, reliable cloud-based learning platform providing services
More informationMS ACCESS DATABASE DATA TYPES
MS ACCESS DATABASE DATA TYPES Data Type Use For Size Text Memo Number Text or combinations of text and numbers, such as addresses. Also numbers that do not require calculations, such as phone numbers,
More informationNOSQL INTRODUCTION WITH MONGODB AND RUBY GEOFF LANE <GEOFF@ZORCHED.NET> @GEOFFLANE
NOSQL INTRODUCTION WITH MONGODB AND RUBY GEOFF LANE @GEOFFLANE WHAT IS NOSQL? NON-RELATIONAL DATA STORAGE USUALLY SCHEMA-FREE ACCESS DATA WITHOUT SQL (THUS... NOSQL) WIDE-COLUMN / TABULAR
More informationIntroduction to Hadoop
Introduction to Hadoop 1 What is Hadoop? the big data revolution extracting value from data cloud computing 2 Understanding MapReduce the word count problem more examples MCS 572 Lecture 24 Introduction
More informationINTRODUCTION TO CASSANDRA
INTRODUCTION TO CASSANDRA This ebook provides a high level overview of Cassandra and describes some of its key strengths and applications. WHAT IS CASSANDRA? Apache Cassandra is a high performance, open
More informationDepartment of Software Systems. Presenter: Saira Shaheen, 227233 saira.shaheen@tut.fi 0417016438 Dated: 02-10-2012
1 MongoDB Department of Software Systems Presenter: Saira Shaheen, 227233 saira.shaheen@tut.fi 0417016438 Dated: 02-10-2012 2 Contents Motivation : Why nosql? Introduction : What does NoSQL means?? Applications
More informationBig data and urban mobility
Big data and urban mobility Antònia Tugores,PereColet Instituto de Física Interdisciplinar y Sistemas Complejos, IFISC(UIB-CSIC) Abstract. Data sources have been evolving the last decades and nowadays
More informationTransactions and ACID in MongoDB
Transactions and ACID in MongoDB Kevin Swingler Contents Recap of ACID transactions in RDBMSs Transactions and ACID in MongoDB 1 Concurrency Databases are almost always accessed by multiple users concurrently
More informationAn Open Source NoSQL solution for Internet Access Logs Analysis
An Open Source NoSQL solution for Internet Access Logs Analysis A practical case of why, what and how to use a NoSQL Database Management System instead of a relational one José Manuel Ciges Regueiro
More informationThe 3 questions to ask yourself about BIG DATA
The 3 questions to ask yourself about BIG DATA Do you have a big data problem? Companies looking to tackle big data problems are embarking on a journey that is full of hype, buzz, confusion, and misinformation.
More informationHacettepe University Department Of Computer Engineering BBM 471 Database Management Systems Experiment
Hacettepe University Department Of Computer Engineering BBM 471 Database Management Systems Experiment Subject NoSQL Databases - MongoDB Submission Date 20.11.2013 Due Date 26.12.2013 Programming Environment
More informationServices. Relational. Databases & JDBC. Today. Relational. Databases SQL JDBC. Next Time. Services. Relational. Databases & JDBC. Today.
& & 1 & 2 Lecture #7 2008 3 Terminology Structure & & Database server software referred to as Database Management Systems (DBMS) Database schemas describe database structure Data ordered in tables, rows
More informationPetabyte Scale Data at Facebook. Dhruba Borthakur, Engineer at Facebook, XLDB Conference at Stanford University, Sept 2012
Petabyte Scale Data at Facebook Dhruba Borthakur, Engineer at Facebook, XLDB Conference at Stanford University, Sept 2012 Agenda 1 Types of Data 2 Data Model and API for Facebook Graph Data 3 SLTP (Semi-OLTP)
More informationScalable Data Analysis in R. Lee E. Edlefsen Chief Scientist UserR! 2011
Scalable Data Analysis in R Lee E. Edlefsen Chief Scientist UserR! 2011 1 Introduction Our ability to collect and store data has rapidly been outpacing our ability to analyze it We need scalable data analysis
More informationParallel Replication for MySQL in 5 Minutes or Less
Parallel Replication for MySQL in 5 Minutes or Less Featuring Tungsten Replicator Robert Hodges, CEO, Continuent About Continuent / Continuent is the leading provider of data replication and clustering
More informationthese three NoSQL databases because I wanted to see a the two different sides of the CAP
Michael Sharp Big Data CS401r Lab 3 For this paper I decided to do research on MongoDB, Cassandra, and Dynamo. I chose these three NoSQL databases because I wanted to see a the two different sides of the
More informationWeb Application Deployment in the Cloud Using Amazon Web Services From Infancy to Maturity
P3 InfoTech Solutions Pvt. Ltd http://www.p3infotech.in July 2013 Created by P3 InfoTech Solutions Pvt. Ltd., http://p3infotech.in 1 Web Application Deployment in the Cloud Using Amazon Web Services From
More informationa year with a talk by Armin '@mitsuhiko' Ronacher for PyGrunn 2013
a year with mongodb a talk by Armin '@mitsuhiko' Ronacher for PyGrunn 2013 That's me. I do Computers. Currently at Fireteam / Splash Damage. We do Internet for Pointy Shooty Games. let's not beat around
More informationPerformance Analysis of Cloud Relational Database Services
Performance Analysis of Cloud Relational Database Services Jialin Li lijl@cs.washington.edu Naveen Kr. Sharma naveenks@cs.washington.edu June 7, 203 Adriana Szekeres aaazs@cs.washington.edu Introduction
More informationHadoop: A Framework for Data- Intensive Distributed Computing. CS561-Spring 2012 WPI, Mohamed Y. Eltabakh
1 Hadoop: A Framework for Data- Intensive Distributed Computing CS561-Spring 2012 WPI, Mohamed Y. Eltabakh 2 What is Hadoop? Hadoop is a software framework for distributed processing of large datasets
More informationHigh Availability Solutions for the MariaDB and MySQL Database
High Availability Solutions for the MariaDB and MySQL Database 1 Introduction This paper introduces recommendations and some of the solutions used to create an availability or high availability environment
More informationReferences. Introduction to Database Systems CSE 444. Motivation. Basic Features. Outline: Database in the Cloud. Outline
References Introduction to Database Systems CSE 444 Lecture 24: Databases as a Service YongChul Kwon Amazon SimpleDB Website Part of the Amazon Web services Google App Engine Datastore Website Part of
More informationIntroduction to Database Systems CSE 444
Introduction to Database Systems CSE 444 Lecture 24: Databases as a Service YongChul Kwon References Amazon SimpleDB Website Part of the Amazon Web services Google App Engine Datastore Website Part of
More informationL7_L10. MongoDB. Big Data and Analytics by Seema Acharya and Subhashini Chellappan Copyright 2015, WILEY INDIA PVT. LTD.
L7_L10 MongoDB Agenda What is MongoDB? Why MongoDB? Using JSON Creating or Generating a Unique Key Support for Dynamic Queries Storing Binary Data Replication Sharding Terms used in RDBMS and MongoDB Data
More informationStructured Data Storage
Structured Data Storage Xgen Congress Short Course 2010 Adam Kraut BioTeam Inc. Independent Consulting Shop: Vendor/technology agnostic Staffed by: Scientists forced to learn High Performance IT to conduct
More informationSawmill Log Analyzer Best Practices!! Page 1 of 6. Sawmill Log Analyzer Best Practices
Sawmill Log Analyzer Best Practices!! Page 1 of 6 Sawmill Log Analyzer Best Practices! Sawmill Log Analyzer Best Practices!! Page 2 of 6 This document describes best practices for the Sawmill universal
More informationLeveraging the Power of SOLR with SPARK. Johannes Weigend QAware GmbH Germany pache Big Data Europe September 2015
Leveraging the Power of SOLR with SPARK Johannes Weigend QAware GmbH Germany pache Big Data Europe September 2015 Welcome Johannes Weigend - CTO QAware GmbH - Software architect / developer - 25 years
More informationChallenges for Data Driven Systems
Challenges for Data Driven Systems Eiko Yoneki University of Cambridge Computer Laboratory Quick History of Data Management 4000 B C Manual recording From tablets to papyrus to paper A. Payberah 2014 2
More informationINTRODUCING DRUID: FAST AD-HOC QUERIES ON BIG DATA MICHAEL DRISCOLL - CEO ERIC TSCHETTER - LEAD ARCHITECT @ METAMARKETS
INTRODUCING DRUID: FAST AD-HOC QUERIES ON BIG DATA MICHAEL DRISCOLL - CEO ERIC TSCHETTER - LEAD ARCHITECT @ METAMARKETS MICHAEL E. DRISCOLL CEO @ METAMARKETS - @MEDRISCOLL Metamarkets is the bridge from
More informationMySQL: Cloud vs Bare Metal, Performance and Reliability
MySQL: Cloud vs Bare Metal, Performance and Reliability Los Angeles MySQL Meetup Vladimir Fedorkov, March 31, 2014 Let s meet each other Performance geek All kinds MySQL and some Sphinx Working for Blackbird
More informationMongoDB. Or how I learned to stop worrying and love the database. Mathias Stearn. N*SQL Berlin October 22th, 2009. 10gen
What is? Or how I learned to stop worrying and love the database 10gen N*SQL Berlin October 22th, 2009 What is? 1 What is? Document Oriented JavaScript Enabled Fast, Scalable, Available, and Reliable 2
More informationCloud Computing For Bioinformatics
Cloud Computing For Bioinformatics Cloud Computing: what is it? Cloud Computing is a distributed infrastructure where resources, software, and data are provided in an on-demand fashion. Cloud Computing
More informationNoSQL Database - mongodb
NoSQL Database - mongodb Andreas Hartmann 19.10.11 Agenda NoSQL Basics MongoDB Basics Map/Reduce Binary Data Sets Replication - Scaling Monitoring - Backup Schema Design - Ecosystem 19.10.11 2 NoSQL Database
More informationNoSQL replacement for SQLite (for Beatstream) Antti-Jussi Kovalainen Seminar OHJ-1860: NoSQL databases
NoSQL replacement for SQLite (for Beatstream) Antti-Jussi Kovalainen Seminar OHJ-1860: NoSQL databases Background Inspiration: postgresapp.com demo.beatstream.fi (modern desktop browsers without
More informationMONGODB - THE NOSQL DATABASE
MONGODB - THE NOSQL DATABASE Akhil Latta Software Engineer Z Systems, Mohali, Punjab MongoDB is an open source document-oriented database system developed and supported by 10gen. It is part of the NoSQL
More informationBig Data Hive! 2013-2014 Laurent d Orazio
Big Data Hive! 2013-2014 Laurent d Orazio Introduction! Context Parallel computation on large data sets on commodity hardware Hadoop [hadoop] Definition Open source implementation of MapReduce [DG08] Objective
More informationSpring,2015. Apache Hive BY NATIA MAMAIASHVILI, LASHA AMASHUKELI & ALEKO CHAKHVASHVILI SUPERVAIZOR: PROF. NODAR MOMTSELIDZE
Spring,2015 Apache Hive BY NATIA MAMAIASHVILI, LASHA AMASHUKELI & ALEKO CHAKHVASHVILI SUPERVAIZOR: PROF. NODAR MOMTSELIDZE Contents: Briefly About Big Data Management What is hive? Hive Architecture Working
More informationChapter 11 Map-Reduce, Hadoop, HDFS, Hbase, MongoDB, Apache HIVE, and Related
Chapter 11 Map-Reduce, Hadoop, HDFS, Hbase, MongoDB, Apache HIVE, and Related Summary Xiangzhe Li Nowadays, there are more and more data everyday about everything. For instance, here are some of the astonishing
More informationWhere We Are. References. Cloud Computing. Levels of Service. Cloud Computing History. Introduction to Data Management CSE 344
Where We Are Introduction to Data Management CSE 344 Lecture 25: DBMS-as-a-service and NoSQL We learned quite a bit about data management see course calendar Three topics left: DBMS-as-a-service and NoSQL
More informationAn Approach to Implement Map Reduce with NoSQL Databases
www.ijecs.in International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 4 Issue 8 Aug 2015, Page No. 13635-13639 An Approach to Implement Map Reduce with NoSQL Databases Ashutosh
More informationFREE computing using Amazon EC2
FREE computing using Amazon EC2 Seong-Hwan Jun 1 1 Department of Statistics Univ of British Columbia Nov 1st, 2012 / Student seminar Outline Basics of servers Amazon EC2 Setup R on an EC2 instance Stat
More information